Tractable POMDP Representations for Intelligent Tutoring Systems

    Authors

    J. T. Folsom-Kovarik; G. Sukthankar;S. Schatz

    Comments

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    Abbreviated Journal Title

    ACM Trans. Intell. Syst. Technol.

    Keywords

    Design; Performance; Experimentation; Partially observable Markov; decision processes; computer-based training; intelligent tutoring; systems; MARKOV DECISION-PROCESSES; PROBLEM-SOLVING ENVIRONMENTS; BAYESIAN; NETWORKS; CATEGORIZATION; SKILL; Computer Science, Artificial Intelligence; Computer Science, Information; Systems

    Abstract

    With Partially Observable Markov Decision Processes (POMDPs), Intelligent Tutoring Systems (ITSs) can model individual learners from limited evidence and plan ahead despite uncertainty. However, POMDPs need appropriate representations to become tractable in ITSs that model many learner features, such as mastery of individual skills or the presence of specific misconceptions. This article describes two POMDP representations-state queues and observation chains-that take advantage of ITS task properties and let POMDPs scale to represent over 100 independent learner features. A real-world military training problem is given as one example. A human study (n = 14) provides initial validation for the model construction. Finally, evaluating the experimental representations with simulated students helps predict their impact on ITS performance. The compressed representations can model a wide range of simulated problems with instructional efficacy equal to lossless representations. With improved tractability, POMDP ITSs can accommodate more numerous or more detailed learner states and inputs.

    Journal Title

    Acm Transactions on Intelligent Systems and Technology

    Volume

    4

    Issue/Number

    2

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    22

    WOS Identifier

    WOS:000321223100011

    ISSN

    2157-6904

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